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Fixing CLIP’s Blind Spots: How New Research Tackles AI’s Visual Misinterpretations
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Author: Harpreet Sahota (Hacker in Residence at Voxel51)
Overview
The paper “Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs” investigates the visual question-answering (VQA) capabilities of advanced multimodal large language models (MLLMs), particularly focusing on GPT-4V. It highlights systematic shortcomings in these models’ visual understanding and proposes a benchmark for evaluating their performance.
The authors introduce the Multimodal Visual Patterns (MMVP) benchmark and propose a Mixture of Features (MoF) approach to improve visual grounding in MLLMs.
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advanced ai author blind blind spots capabilities computervision datascience gpt hacker language language models large llms machinelearning mllms multimodal question research understanding